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AI-optimized detector design for the future Electron-Ion Collider: the dual-radiator RICH case
Name
1911.05797.pdf
Description
Accepted version
Size
6.07 MB
Format
Adobe PDF
Checksum (MD5)
e63a822e5fa70b7556116cf915c4c914
Date Issued
2020
Journal
Journal of Instrumentation
Publisher
IOP Publishing
Version
Author's final manuscript
Abstract
© 2020 IOP Publishing Ltd and Sissa Medialab. Advanced detector R&D requires performing computationally intensive and detailed simulations as part of the detector-design optimization process. We propose a general approach to this process based on Bayesian optimization and machine learning that encodes detector requirements. As a case study, we focus on the design of the dual-radiator Ring Imaging Cherenkov (dRICH) detector under development as a potential component of the particle-identification system at the future Electron-Ion Collider (EIC). The EIC is a US-led frontier accelerator project for nuclear physics, which has been proposed to further explore the structure and interactions of nuclear matter at the scale of sea quarks and gluons. We show that the detector design obtained with our automated and highly parallelized framework outperforms the baseline dRICH design within the assumptions of the current model. Our approach can be applied to any detector R&D, provided that realistic simulations are available.
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
10.1088/1748-0221/15/05/P05009
